Linking Lead and Education Data in Connecticut

8/2/2019 Linking Lead and Education Data in Connecticut

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The Impact of Early Childhood Lead Exposure

on Educational Test Performance among

Connecticut Schoolchildren, Phase 1 Report

14 February 2011

Marie Lynn Miranda, Dohyeong Kim, Claire Osgood, and Douglas Hastings

Childrens Environmental Health Initiative

Box 90328, Duke University, Durham, NC 27708

[email protected]

www.nicholas.duke.edu/cehi

919-613-8723
mailto:[email protected]:[email protected]://www.nicholas.duke.edu/cehihttp://www.nicholas.duke.edu/cehihttp://www.nicholas.duke.edu/cehimailto:[email protected]


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Motivation for the Project

Researchers at Duke Universitys Childrens Environmental Health Initiative (CEHI) were

contacted by state agency representatives from the State of Connecticut about undertaking an

analysis of the effects of early childhood lead exposure on test performance among Connecticut

schoolchildren. CEHI researchers had previously conducted similar analysis on data from NorthCarolina (see below). The relevant data were provided to CEHI after all research approvals were

obtained (both from the State of Connecticut and the Duke University Institutional Review

Board). This report presents the results of the CEHI analysis of Connecticut lead and education

data.

Introduction

Although much progress has been made, childhood lead poisoning remains a critical

environmental health concern. Since the late 1970s, mounting research has demonstrated that

lead causes irreversible, asymptomatic neurocognitive effects in children at levels far belowthose previously considered safe. Thus, between 1960 and 1991, the CDC incrementally

lowered its blood lead action level in children by 88%, from 60 to 10 g/dL (Centers for Disease

Control and Prevention, 2005). According to 20072008 National Health and Nutrition

Examination Survey (NHANES) survey data, 1.3% of 1 to 5-year-olds in the United States had

blood lead levels at or above the current CDC blood lead action level (National Center for Health

Statistics, 2010).

Childhood lead exposure has been linked to a number of adverse cognitive outcomes, including

reduced performance on standardized IQ tests (7-13), decreased performance on cognitive

functioning tests (14), adverse neuropsychological outcomes (15), neurobehavioral deficits (16),decreased end-of-grade (EOG) test scores (17) and classroom attention deficit behavior (18).

Moreover, research has linked lead exposure at levels markedly below the blood lead action level

of 10 g/dL to cognitive and socio-behavioral impacts in children (Bellinger, Stiles, &

Needleman, 1992; Canfield et al., 2003; Chiodo, Jacobson, & Jacobson, 2004; Dietrich, Berger,

Succop, Hammond, & Bornschein, 1993; Schnaas et al., 2006; Tong, Baghurst, McMichael,

Sawyer, & Mudge, 1996). For example, in a study of 380 school age children, Dudek and

Merecz (1997) found that the steepest declines in standardized IQ test performance occur in

children with blood lead levels between 5 and 10 g/dL. Similar studies have further

emphasized the deleterious nature of lead exposure at levels below 10 g/dL (Lanphear et al.,

2005; Needleman & Landrigan, 2004; Schnaas et al., 2006; Schwartz, 1993).

Previous research at CEHI found an association between blood lead levels among children in

North Carolina and their educational outcomes, as measured by end of grade (EOG) test scores.

The detrimental effect of lead on EOG test scores was observed at levels markedly below 10

g/dL. For example, in a study based on blood lead surveillance and educational testing data for

seven North Carolina counties (2007), lead levels as low as 2 g/dL showed a discernible impact

on test scores. For both reading and mathematics, the magnitude of the average test score


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decrement associated with a blood lead level of 5 g/dL was comparable that of a measure of

household income (student enrollment status in free or reduced lunch programs) - a risk factor

well known to be important to child educational outcomes. CEHI later replicated these findings

based on all 100 counties in North Carolina (Miranda, Kim, Reiter, Overstreet Galeano, &

Maxson, 2009).

In this report, we use the analytical approach employed in North Carolina as the basis for

examining the association between blood lead levels and educational outcomes among

Connecticut school children.

Methodology

Data Acquisition and Preparation

Tracy Hung, an epidemiologist with the Lead Poisoning Prevention and Control Program,

Connecticut Department of Public Health, provided CEHI with identifier information (includingname, date of birth, county, gender, and race) coupled with a child ID code for children born

between 1996 and 2002 from the Connecticut Vital Records System. Richard Mooney, with the

Department of Education, provided data on third, fourth, and fifth grade test scores in

Connecticut during the 2007-2008 and 2008-2009 school years from the Connecticut Mastery

Test (CMT) results. We matched records between the two data sets using the childs first name,

last name, date of birth, sex, and county of residence together to form a unique identifier. A

childs records for the two data sets were matched if they met any of the following criteria:

1. First name, last name, date of birth, sex, and county matched exactly.2. First name, last name, date of birth, and sex matched exactly, while the county field was

inconsistent or not present.

3. First name, date of birth, sex, and county matched exactly, while the last name was eitherclose in spelling (using the SPEDIS function) or a subset (such as Smith and Smith-

Jones).

4. Last name, date of birth, sex, and county matched exactly, while the first name was eitherclose in spelling (using the SPEDIS function) or a subset (such as Mary and Mary

Lou).

5. First name, last name, date of birth, and county matched exactly, while sex wasinconsistent or not present. In this case, race/ethnicity must have been consistent or not

present for us to consider the records a match.

We returned the child ID codes for the matched children to Ms. Hung, who then supplied CEHI

with the blood lead screening results for any child within this group with at least one blood lead

test. Using the maximum recorded lead value for children with multiple tests, the blood lead

results were then joined to the CMT scores, yielding 146,175 records with both blood lead and


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test score information. These 146,175 records corresponded to 98,009 unique children (a child

can have a record in both of the school years) with both blood lead and CMT results.

After linking the blood lead and EOG data, we restricted the dataset to non-Hispanic black

(NHB) and non-Hispanic white (NHW) children who were in fourth grade during either the

2007-2008 or 2008-2009 school years, had been screened for lead before age seven, and did not

have limited English proficiency. These restrictions produced a dataset with 34,935 fourth grade

children with reading test results and 35,196 fourth grade children with mathematics test results

who had also been screened for lead.

Statistical Analysis

We examined the relationship between blood lead levels and end-of-grade test scores for fourth

grade children. Initial exploratory analysis included comparing blood lead levels and test scores

graphically and generating tables of summary statistics. We conducted a multivariable ordinary

least squares regression in order to determine the importance of blood lead levels to mean testscores, while controlling for individual level characteristics commonly understood to be

associated with educational outcomes. Such factors included race, sex, enrollment in free or

reduced lunch programs, and enrollment in special education. We also included dummy

variables representing the school district for each record in order to account for unmeasured

district level factors that may be associated with individual educational outcomes, such as

socioeconomic level.

All statistical analyses were conducted using STATA 9.2 (StataCorp., College Station, TX).

Results

Exploratory Analysis

Table 1 shows the distribution of children with mathematics scores across different blood lead

levels, disaggregated by race. Of the 35,196 NHW or NHB children with mathematics scores,

21.5% were NHB and 78.5% were NHW. If exposure to lead were evenly distributed across the

population, then we would expect to see roughly this same split (21.5%/78.5%) at all blood lead

levels. What is apparent from Table 1, however, is that NHB children are under-represented in

the low blood lead categories (0-2) and over-represented in the high blood lead categories (3-10+) relative to the total screened children. Conversely, NHW children are over-represented in

the low blood lead categories (0-2) and under-represented in the high blood lead categories (3-

10+) relative to the total screened children. An important implication of this pattern is that if

early childhood lead exposure does affect performance on the CMT (which we will demonstrate

below), then the impact of the environmental exposure will accrue more acutely among NHB


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children because they are more likely to be exposed and exposed at high levels. The distribution

for children with reading scores shows a similar trend (data not shown).

The data from Table 1 are presented in histogram format in Figure 1. Compared to the

distribution for early childhood lead exposure among NHW children, the distribution for NHB

children has a higher variance and is left-skewed. The Mantel-Haenszel Chi-square test for

equality of distribution indicates that the sample distributions by race are significantly different

from each other (p

10

Percentage

Blood Lead Level

Percent

Black

Percent

White

Table 1. Blood lead levels for fourth graders with mathematics scores disaggregated by race

Number and Percentage of NHB and NHW Children with Mathematics Scores

by Blood Lead LevelNumber NHB Percent NHB Number NHW Percent NHW

Bll = 0 1749 8.9 17936 91.1

0


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Figure 2 graphs reading CMT scores by blood lead levels for all matched students by race. A

clear negative relationship between CMT scores and blood lead levels is apparent. Lower blood

lead levels are associated with higher test scores, and higher blood lead levels are associated with

lower test scores, with some erratic behavior at blood lead levels of 9+, likely due to the small

sample sizes in this range (see Table 1). Figure 3 demonstrates a similar trend for mathematics

scores.

Table 2. Summary statistics for reading test results

Reading Data (N=34,935) MeanStan.Dev Median

Minimum

Maximum

Reading Score 254.56 43.38 257 100 400

Lead 2.4 4.88 0 0 81

Male (1=male, 0=female) 0.51 0.5 1 0 1Black (1=black,0=white) 0.22 0.41 0 0 1

Free/reduced lunch

(1=enrolled, 0=not enrolled) 0.27 0.45 0 0 1

Age at screen 2.39 1.44 2.02 0 7

Special education

(1=received, 0=not received) 0.1 0.29 0 0 1

Table 3. Summary statistics for mathematics test results

Mathematics Data

(N=35,196) MeanStan.Dev Median Minimum Maximum

Mathematics Score 262.6 48.79 263 100 400Lead 2.41 4.88 0 0 81

Male (1=male, 0=female) 0.51 0.5 1 0 1

Black (1=black,0=white) 0.22 0.41 0 0 1

Free/reduced lunch

(1=enrolled, 0=not enrolled) 0.28 0.45 0 0 1

Age at screen 2.39 1.45 2.02 0 7

Special education(1=received, 0=not received) 0.1 0.3 0 0 1


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Figures 4 and 5 examine the low end of the achievement scale: failure rates on the reading and

mathematics sections of CMT, respectively. Children with low blood lead levels in early

childhood have lower failure rates on both the mathematics and reading CMT than children with

high blood levels. Similarly, children with high blood lead levels in early childhood have higher

failure rates than those with low blood levels. Moreover, for both NHB and NHW children, a

dose-response relationship between blood lead levels and failure on the CMT is evident for blood

lead levels between about 1 and 6.

Figure 3. Mean mathematics score vs. blood lead level by race

200

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290

BLL

= 0

0

Linking Lead and Education Data in Connecticut

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